Reviews: Beyond Parity: Fairness Objectives for Collaborative Filtering

In this paper the authors explore different metrics for measuring fairness in recommender systems. In particular, they offer 4 different metrics that measure if a recommender system over-estimates or under-estimates how much a group of users will like a particular genre. Further, they show that by regularizing the by the discrepancy across groups does not hurt the model accuracy while improving fairness on the metric used for the regularization (as well as undiscussed slight effects on the other fairness metrics). In contrast to the work of Hardt et al, this paper focuses on a regression task (rating prediction) and explores the difference between absolute and relative errors. This is an interesting point in recommender systems that could easily be overlooked by other metrics.